A novel computational model for cerebral blood flow rate control mechanisms to evaluate physiological cases

Selim Bozkurt, A. Volkan Yilmaz, Kaushiki Bakaya, Aniket Bharadwaj, Koray K. Safak

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6 Citations (Scopus)
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In this study, a new numerical model simulating interaction among static cerebral autoregulation, cerebrovascular CO2 and O2 reactivities and systemic peripheral resistance regulation was developed and integrated into a cardiovascular system model including heart chambers, systemic and pulmonary circulations, and cerebral circulation with Circle of Willis. Simulations were performed to evaluate cerebral blood flow in a healthy condition, effect of altered static cerebral autoregulation in heart failure, effect of impaired static cerebral autoregulation in malignant hypertension, effects of arterial CO2 and O2 pressures in hypercapnia, hypocapnia and hypoxemia on cerebral blood flow. Also, sensitivity analysis was performed to assess influence of arterial CO2 and O2 pressure, aortic pressure set point in the cerebral flow autoregulatory function, systemic peripheral resistance, left ventricular active and passive properties on the cerebral blood flow rate. There was a high and positive correlation between cerebral blood flow rate and arterial CO2 pressure whereas left ventricular contractility influenced cerebral blood flow rate slightly. Cerebral blood flow rate in the healthy condition, heart failure, malignant hypertension, hypercapnia, hypocapnia and hypoxemia was 721 mL/min, 606 mL/min, 1313 mL/min, 950 mL/min, 504 mL/min and 972 mL/min, respectively. Simulation results and sensitivity analysis showed that the new numerical model can be used to evaluate cerebral blood flow in various physiological cases.
Original languageEnglish
Article number103851
Number of pages14
JournalBiomedical Signal Processing and Control
Early online date14 Jun 2022
Publication statusPublished (in print/issue) - 1 Sept 2022

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  • Cerebral blood flow rate regulation
  • Cardiovascular system
  • Cerebral circulation
  • Numerical modelling
  • Physiological control systems


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